PhD Thesis Presentation

Characterisation of Intertidal Vegetation on European Coasts Using MultiScale Remote Sensing in Response to Natural and Anthropogenic Pressures

The 15th of May 2025

Thesis supervisor:

Laurent Barillé, Professor

Co-supervisor:

Pierre Gernez, Lecturer

Jury members:

Antoine Collin

Rodney Forster

Evangelos Spyrakos

Bárbara Ondiviela

Federica Braga

Laurent Barillé

Pierre Gernez

Lecturer

Professor

Professor

Senior scientist

Senior Researcher

Professor

Lecturer

École Pratique des Hautes Études (EPHE), Dinard, France

University of Hull, United Kingdom

University of Stirling, United Kingdom

Universidad de Cantabria, Spain

CNR-ISMAR, Venice, Italy

Nantes Université, France

Nantes Université, France

Simon Oiry

Table of Contents

Introduction

Coastal Environments

Areas where the land masses meet the seas

Interface regions where land and sea meet

  • Directly in contact with the sea
  • 25km away from the sea
  • 50km away from the sea
  • French Coast are densely populated:
  • 4% of the French territory
  • 10% of the French population
  • Globally:

1 billion people (15%) within 10km (4%)

~3 billion by 2100

Human activities

Hotspots of Economic Growth

Seaport

Dredging

Aquaculture

Energy Production

Artificialisation

  • Fishing activities
  • Tourism
  • Industries

Environmental Impacts

The mark of human activity on nature

Oil spills

Erosion

Alien Species Introduction

Climate change

Habitat destruction

  • Fishing activities
  • Pollutions

Intertidal habitats

Living on the edge of land and sea

Areas between high and low tide

Saltmarshes

Mangroves

Polychaete reefs

Rocky reefs

Tidal flats

Oyster reefs

A rich variety of intertidal habitats

Soft-bottom substrates

Guadalquivir River, Spain

  • A - Magnoliopsida
  • B - Bacillariophyceae
  • C - Phaeophyceae
  • D - Florideophyceae
  • E - Chlorophyceae

Five Taxonomic Classes

of Vegetation

Hard-bottom substrates

Vigo, Spain

Saja estuary, Spain

Ecosystem Services

  • Protection against Erosion
  • Carbon fixation
  • Nursery & Shelter
  • Eutrophication mitigation

~ $30 trillion per year

Protect these ecosystems:

  • Habitat Directive (1992)
  • Water framework Directive (2001)
  • Marine Strategy Framework Directive (2008)
  • Birds Directive (2009)
  • Nature Restoration Law (2024)

Good knowledge and monitoring to inform policies

Remote Sensing

A tool to map them all !

Traditional sampling methods:

  • Expensive
  • Time consuming
  • Low extent and temporal resolution
  • Hard to access

Remote Sensing:

  • Cost effective
  • Good coverage/Time ratio
  • Synchronous broad-scale view
  • Simplifies the field work

From the sky to the sea

The science of obtaining information about objects or areas from a distance

Applied to Earth Observation:

Remote Sensing

Fieldwork remains essential to make sense of what satellites see

Radiometric calibration

Aven, France

Ground truthing

Noirmoutier, France

Features georeferencing

Tainaron, Greece

Sampling

Cadiz, Spain

Objectives of this work

Show how remote sensing can effectively map intertidal habitats and assess environmental pressures

Analysing the potential of multispectral sensors to distinguish macrophytes in soft-bottom intertidal zones at low tide

Building an algorithm that discriminates the most common taxonomic classes of vegetation found on soft bottom intertidal sediment

Investigate the capacity of remote sensing to monitor intertidal vegetation under abiotic and biotic pressures

Developing Advanced Methodologies for Intertidal Vegetation Monitoring

Challenges to map intertidal vegetation

Introduction to Spectroradiometry

\[R(\lambda) = \frac{L_{\text{u}}(\lambda)}{L_{\text{d}}(\lambda)}\]

  • \(\lambda\) is the Wavelength
  • \(L_{\text{u}}\) is the upwelling radiance
  • \(L_{\text{d}}\) is the downwelling radiance

\[R_i^*(\lambda) = \frac{R_i(\lambda) - \min(R_i)}{\max(R_i) - \min(R_i)}\]

  • \(R_i(\lambda)\) is the reflectance the the wavelength \(\lambda\) of the spectrum \(R_i\)
  • \(min(R_i)\) and \(max(R_i)\) are the minimum and maximum reflectance of the spectrum \(R_i\)
  • Each spectrum is between 0 and 1

ASD FieldSpec Handheld 2

Hyperspectral Sensor

A lot of Narrow Spectral Bands

  • Is it possible to discriminate green macrophytes using remote sensing techniques ?
  • What is the impact of the spectral resolution on the discrimination accuracy ?

Material & Methods

Building a Spectral library of intertidal vegetation

Total of 332 Spectra of 5 taxonomic classes

Spectral degradation

ASD

PRISMA

Drone

S2 - 20m

Pléiades

S2 - 10m

250 bands

50 Bands

10 Bands

8 Bands

4 Bands

4 Bands

Spectral comparisons

Compare the Spectra:

  • nMDS + ANOSIM for each spectral resolution

Compare the Sensors:

  • Supervised Machine Learning Classifiers
  • Random Forest

Training of the model:

  • 75 % of the dataset
  • Maximisation of the AUC-ROC

Validation of the model:

  • 25 % of the dataset
  • Accuracy metrics

Putting theory into practice

DJI Matrice 200

Micasense RedEdge-MX Dual

50% Spectralon

Downwelling Light Sensor

Results

Hyperspectral library

Hyperspectral library - nMDS

Hyperspectral library - Random Forest Classifier

  • Global accuracy: 0.95
  • Cohen’s kappa: 0.93
  • Sensitivity: 0.93
  • Specificity: 0.98

  • Global accuracy: 0.83
  • Cohen’s kappa: 0.79
  • Sensitivity: 0.84
  • Specificity: 0.96

Drone imagery - Example of classification

Chlorophyceae

Bacillariophyceae

Magnoliopsida

Florideophyceae

Drone imagery - Validation

Drone imagery - Variable importance

Discussion

Pigment Composition, Spectral Signature and Variable Importance

Similar pigment composition,…

  • … but difference in carotenoid to chlorophyll-a ratios (Repolho et al., 2017)
  • … difference in the cell structure and the water content

Distinction between green macrophyte possible using multispectral resolutions, …

  • … but ~530 nm & ~650 nm are key wavelengths

Green macrophytes often co-occurs in intertidal areas…

  • Ultra high spatial resolution (from 80 to 8mm per pixel)

Green macrophytes often co-occurs in intertidal areas…

  • Ultra high spatial resolution (from 80 to 8mm per pixel)
  • Easy Photo-interpretation of pixels
  • More then 1 000 000 training pixels. Over 11 sites of 3 country
  • Diverse training dataset

Drone: 0.26 ha ~ 2.5 millions pixels

S2: 25 000 hectares ~ 2.5x Paris

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Bourgneuf Bay, July 2024

Case Study 1 – Facing Biological Invasions

Ecological Context & Significance

History of the aquaculture of the oyster in Europe

Flat Oyster

Portuguese Oyster

Pacific Oyster

A Hidden Passenger

  • Originated from Japan
  • 10 000 T spat were imported between 1971 and 1973
  • Originated from Japan
  • Transport of fragment of Gracilaria vermiculophylla

Resilient to:

  • Salinity changes
  • Desiccation
  • Eutrophic conditions
  • Can attach to shells, rocks or colonise soft bottom areas

Well adapted to European estuaries

Gracilaria vermiculophylla

First observation in Europe in the Belon, Brittany, in 1996

Belon Estuary, France, 2024

Aveiro, Portugal, 2021

Etel, France, 2024

Auray, France, 2024

Scorff, France, 2024

Saja estuary, Spain, 2024

Ecological Impacts of the invasion

Negatives:

  • Can affect native Fucoids and Seagrasses
  • Alter the sediment composition and structure
  • Modify or disrupt trophic interactions

Positives:

  • Create new habitats
  • Stabilize the Sediment

Monitoring and Managing

Remote Sensing as a tool to follow the invasion

Satellite & Aerial views:

  • Follow the invasion over time
  • Go back in time

Drone:

  • Flexibility to monitor the early stages of the invasion
  • Offer an ultra high resolution

Objectives of the work

Make the first description of G. vermiculophylla spatial distribution using remote sensing techniques

Using RS archives to assess historical invasion in the Belon Estuary

Use DISCOV to map G. vermiculophylla and link its spatial distribution to the mudflat topography.

Material & Methods

Historical analysis

Sciences et Techniques, Nantes, 1962

Maps and Aerial photographs archives

  • 8 images between 1952 and 2012
  • 1 Drone flight in 2024

Photo interpretation of images to retrieve the area covered by G. vermiculophylla

Drone Mapping

DJI Matrice 300

4 Drone flight over G. vermiculophylla

Micasense RedEdge-MX Dual

DJI Zenmuse L1

2 Instruments:

  • Multispectral camera
  • LiDAR

10 Spectral bands between 444 and 840 nm

  • NIR LiDAR
  • 240 000 points/s
  • ~ 3cm accuracy
  • High resolution RGB camera

Digital Surface Model:

  • Map of the Slope of the Mudflat

Generalised Linear Mixed Model

\[ \begin{align*} \mathrm{Cover}_{ij} &\sim \mathrm{Beta}\bigl(\mu_{ij}\,\phi,\,(1-\mu_{ij})\,\phi\bigr),\\[1em] \mu_{ij} &= \mathrm{logit}(\eta_{ij}), \\[1em] \eta_{ij} &= \underbrace{\alpha_j}_{\substack{\text{intercept for}\\\text{site }j}} + \underbrace{\beta_1\,\mathrm{Bathymetry}_{ij}}_{\text{effect of elevation}} + \underbrace{\beta_2\,\mathrm{Slope}_{ij}}_{\text{effect of slope}}. \end{align*} \]

Results

Historical records in the Belon estuary

Topography of the mudflat

RGB Composition

DSM Color Composition

Slope Categorized

Elevation vs Presence of Algae

  • Higher Cover on the Upper Intertidal

  • The steeper the lower the cover

Discussion

First map of the spatial distribution of G. vermiculophylla:

  • It can create large monospecific meadows…
  • … or be mixed with others intertidal vegetations

First map of the spatial distribution of G. vermiculophylla

Drone mapping G. vermiculophylla with machine learning

Saja estuary, Spain

Belon estuary, France

Distribution linked with the topography

  • Inhabit the upper intertidal
  • Resistant to desiccation, light and salinity variations

Distribution linked with the topography

  • Inhabit the upper intertidal
  • Resistant to desiccation, light and salinity variations
  • Inhabit flat areas…
  • …experiencing lower current velocity during tidal exchanges

Invasion phases

Lag Phase

  • Very low abundance
  • Need for genetic or mutualistic adjustment
  • Eradication is feasible and cheapest

Expansion Phase

  • Near‑exponential increase in cover
  • Control effort and cost rise sharply
  • Priority shifts to containment and protection of high‑value sites

Saturation Phase

  • Percent cover reaches a plateau, growth limited by space/resources
  • Ecosystem impacts stabilise but persist
  • Focus turns to long‑term suppression, impact mitigation, and restoration rather than eradication

Short Lag phase

  • Large number of fragments/individuals has been introduced repeatedly in the environment
  • G. vermiculophylla is well adapted to European estuaries and already suited to local climate
  • Ignored by local species, no grasing

Remote Sensing can monitor early stages of the invasion…

  • Making it a powerful tool for early decision making
  • Facilitates timely interventions

Case Study 2 – Mapping the impact of Heatwaves on intertidal seagrasses

Introduction

Browning of seagrasses across Europe

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Aveiro Lagoon, Portugal, June 2022

Quiberon, France, September 2021

What’s in the litterature ?

On subtidal Zostera marina and Cymodocea nodosa:

  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Responses vary greatly between species…
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Responses vary greatly between species…
  • …and within a single species across latitudes.

What about Zostera noltei ?

Impact on the reflectance ?

Impact of Extreme Atmospheric temperature ?

Extreme Temperature Events = Heatwaves

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Hypothesis & Objectives

Heatwaves alter the spectral reflectance of Zostera noltei seagrass. This change can be detected using remote sensing.

  • Evaluate the direct impact of heatwave-induced thermal stress on the reflectance of Zostera noltei through controlled experiments.
  • Develop a spectral index for detecting stress-induced changes in seagrass coloration.
  • To apply findings from experimental reflectance changes to satellite-based remote sensing, assessing the spatial extent and temporal dynamics of an heatwave event that occurs in September 2021, in Quiberon, on seagrass meadows.

Material & Methods

Experiment in the Lab

Intertidal chambers from ElectricBlue

  • Air Temperature : from 18 to 60°C
  • Water Temperature : from 8°C to 55°C
  • Programmable tides
  • Programmable lights

Measure variation of seagrass leaves reflectance over time.

Seagrasses inside of a chamber

Hyperspectral measurement every minute in each tank

Control

Treatment

Satellite Mapping of the impact of Heatwaves on seagrasses

Atmospheric heatwave between the 4th of September 2021 and the 7th of September 2021 in Quiberon

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

  • During: 6th of September 2021

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

  • During: 6th of September 2021

  • After: 8th of October 2021

Both Experimental and Satellite Mapping

  • Well established radiometric Indices:

\[ f''(\lambda_i) \approx \frac{f(\lambda_{i+1}) - 2f(\lambda_i) + f(\lambda_{i-1})}{(\Delta \lambda)^2} \]

\[NDVI = \frac{R(NIR)-R(Red)}{R(NIR)+R(Red)}\]

\[GLI = \frac{[R(Green)-R(Red)]+[R(Green)-R(Blue)]}{(2 \times R(Green) )+ R(Red) + R(Blue) }\]

Results

Spectral signatures:

SHSI design

Heatwave experiment

Spectral metrics:

\(R''_{665 \, \text{nm}}\) drops by 68 %

\(NDVI\) drops by 31 %

\(GLI\) drops by 54 %

\(SHSI\) increases by 420 %

in situ Satellite Mapping

Before

During

Before

During

After

Discussion

Mapping Impacted meadows

Seagrasses impacted by heatwave have a distinct spectral signature

  • Reflectance drops at 560 and 740nm

Thermal stress = Oxydative stress

  • Degradation of Chlorophyll-a
  • Shift in pigment ratios = Carotenoids becoming more prominent

Oxydative stress = Membrane damages

Possible to detect seagrass thermal stress using satellite remote sensing, using SHSI

  • Most Sensitive early-metric to flag damage over meadows
  • Designed to be used by most space missions (Sentinel-2, Pléiades, Wordview-3, SkySat, GeoSat-2…)
  • but also by future missions (Sentinel-2 Next Generation, LandSat Next)

Satellite mapping reveals tide-modulated, patchy impacts

  • Upper intertidal areas are more impacted
  • 95% of darkened patches are above 3.9 m
  • Darkening occurs in seagrasses exposed more than 13 h per day

Rapid global escalation of HW frequency, intensity and duration

  • How does increasing heatwave pressure affect recovery capacity of Z. noltii?
  • Can seagrasses maintain resilience under more frequent aerial exposure?
  • Are upper-intertidal meadows approaching a tipping point?

General conclusions and future perspectives

Macrophyte Discrimination: Successes & Challenges

Key Success: Multispectral RS + ML effectively differentiate intertidal vegetation, even with similar pigments (e.g., seagrass vs. green macroalgae).

Core Mechanism: Relies on detecting subtle spectral variations (pigment proportions & concentrations).

DISCOV Algorithm – A Key Output:

  • Dynamic, adaptable, and open-source (GitHub).
  • Challenge: Initial underperformance with Florideophyceae due to limited training data.
  • Solution: Expanded training dataset significantly improved model accuracy & robustness.

Ongoing Challenge: Natural variability in pigment content (phenology, stress, intraspecies variation).

Future Direction: Continue expanding the training dataset (broader geographic/temporal range) to enhance algorithm generalisability.

Drone Technologies in Coastal Monitoring

Significance of UAVs (Drones):

  • Provide very high-resolution spatial data.
  • Offer flexible and on-demand deployment.
  • Crucial for capturing fine-scale habitat heterogeneity and rapid environmental changes.

Applications Demonstrated in Associated Research:

Aquaculture Monitoring: Estimating Kappaphycus alvarezii biomass & carrageenan (Nurdin et al., 2023).

Applications Demonstrated in Associated Research:

Oyster Farm Assessment: Mapping intertidal oyster farm structures, mesh bag sizes, & table heights (A. Román et al., 2023).

Drone and Satellite Interactions: A Powerful Synergy

Complementary Strengths:

  • UAVs: Unmatched spatial resolution, ground-truth validation, training data generation.
  • Satellites: Broad spatial coverage, consistent temporal monitoring for large-scale trends.

Example: The ICE CREAMS Model (Davies et al., 2024a,b):

  • Methodology: DISCOV (drone-derived) classifications used to train and validate ICE CREAMS (Sentinel-2 satellite) model.

  • Achievements:

    • Mapped intertidal seagrass across diverse European sites.
    • Revealed latitudinal gradients in seagrass phenology.
    • Tracked inter-annual trends in seagrass extent.

Key Benefit: This synergistic approach balances local accuracy with regional/global scalability, optimising monitoring efforts.

Assessing Coastal Ecosystem Threats via RS

RS for Comprehensive Threat Monitoring: Addresses impacts of climate change (heatwaves), eutrophication, invasive species, and habitat fragmentation.

Key Tools & Techniques Used/Discussed:

  • Multispectral/Hyperspectral Imaging: Assessing vegetation health, species composition, stress indicators.
    • Chapter 4: Mapping invasive Gracilaria vermiculophylla.
    • Chapter 5: Quantifying heatwave impacts on seagrass using the Seagrass Heat Shock Index (SHSI).
  • LiDAR: Precise topographic data (elevation, slope) influencing species distribution.
  • Vegetation Indices (NDVI, SHSI): Early detection of ecosystem degradation.
  • Machine Learning: Advanced detection, classification, and predictive modeling.

Broader Application: RS data can be integrated into risk assessment frameworks (e.g., DAPSI(W)R(M)).

Outcome: Provides a holistic understanding of coastal ecosystem dynamics, crucial for informed management and conservation.

Future Perspectives: Seagrass Restoration

Critical Context: Widespread global decline of seagrass meadows due to anthropogenic pressures necessitates urgent and effective restoration efforts.

Restoration Challenges: Success is variable (e.g., Arcachon Bay - Laurent, 2024; initial survival, then significant mortality).

How Remote Sensing Can Revolutionize Restoration:

  • Site Selection: Mapping habitat suitability (water quality, depth, substrate) & assessing pressures to identify optimal locations.
  • Monitoring Success: Tracking changes in coverage, density, and health of restored meadows.
  • Adaptive Management: Providing data to refine techniques & improve long-term outcomes.

DISCOV Model’s Role in Seagrass Restoration:

  • Precise Classification: Aids in selecting sites with minimal competition.
  • Fine-Scale Monitoring: Detailed assessment of restored patches.

Upcoming Application: EU Project REBORN (Interreg Program):

DISCOV and ICE CREAMS will be applied to:

  • Track seagrass distribution across North-West Europe.
  • Identify at-risk areas and prioritize restoration interventions.
  • Support transboundary data sharing and collaborative management.

Overall Conclusion

  • This PhD advanced multi-scale RS techniques for characterising intertidal vegetation.
  • Developed novel tools (DISCOV, SHSI) and provided key insights for:
    • Monitoring ecosystem health and biodiversity.
    • Understanding and quantifying threats (invasive species, heatwaves).
    • Guiding targeted conservation and restoration strategies.
  • The integration of drone and satellite data, powered by machine learning, offers robust solutions for managing complex and dynamic coastal ecosystems.

Acknowledgements

  • Supervisors
  • Jury Members
  • Colleagues
  • Funding Institutions

Questions?

Thank you for your attention.

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